Inference for proportional hazard model with propensity score

Bo Lu, Dingjiao Cai, Luheng Wang, Xingwei Tong, Huiyun Xiang

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Since the publication of the seminal paper by Cox (1972), proportional hazard model has become very popular in regression analysis for right censored data. In observational studies, treatment assignment may depend on observed covariates. If these confounding variables are not accounted for properly, the inference based on the Cox proportional hazard model may perform poorly. As shown in Rosenbaum and Rubin (1983), under the strongly ignorable treatment assignment assumption, conditioning on the propensity score yields valid causal effect estimates. Therefore we incorporate the propensity score into the Cox model for causal inference with survival data. We derive the asymptotic property of the maximum partial likelihood estimator when the model is correctly specified. Simulation results show that our method performs quite well for observational data. The approach is applied to a real dataset on the time of readmission of trauma patients. We also derive the asymptotic property of the maximum partial likelihood estimator with a robust variance estimator, when the model is incorrectly specified.

Original languageEnglish (US)
Pages (from-to)2908-2918
Number of pages11
JournalCommunications in Statistics - Theory and Methods
Volume47
Issue number12
DOIs
StatePublished - Jun 18 2018

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

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